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Background Evidence supporting the effectiveness of care management programs for complex patients has been inconclusive. However, past reviews have not focused on complexity primarily defined by multimorbidity and healthcare utilization. We conducted a systematic review of care management interventions targeting the following three patient groups: adults with two or more chronic medical conditions, adults with at least one chronic medical condition and concurrent depression, and adults identified based solely on high past or predicted healthcare utilization. Methods Eligible studies were identified from PubMed, published between 06/01/2005 and 05/31/2015, and reported findings from a randomized intervention that tested a comprehensive, care management intervention. Identified interventions were grouped based on the three “complex” categories of interest (described above). Two investigators extracted data using a structured abstraction form and assessed RCT quality. Results We screened 989 article titles for eligibility from which 847 were excluded. After reviewing the remaining 142 abstracts, 83 articles were excluded. We reviewed the full-text of 59 full-text articles and identified 15 unique RCTs for the final analysis. Of these 15 studies, two focused on patients with two or more chronic medical conditions, seven on patients with at least one chronic medical condition and depression, and six on patients with high past or predicted healthcare utilization. Measured outcomes included utilization, chronic disease measures, and patient-reported outcomes. The seven studies targeting patients with at least one chronic medical condition and depression demonstrated significant improvement in depression symptoms (ranging from 9.2 to 48.7% improvement). Of the six studies that focused on high utilizers, two showed small reductions in utilization. The quality of the research methodology in most of the studies (12/15) was rated fair or poor. Conclusions Interventions were more likely to be successful when patients were selected based on having at least one chronic medical condition and concurrent depression, and when patient-reported outcomes were assessed. Future research should focus on the role of mental health in complex care management, finding better methods for identifying patients who would benefit most from care management, and determining which intervention components are needed for which patients.

Medically complex patients are a heterogeneous group that contribute to a substantial proportion of health care costs. Coordinated efforts to improve care and reduce costs for this patient population have had limited success to date. To define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters. This cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles. Complex patient profiles, 1-year health care utilization, and mortality outcomes by profile. The analysis included 104 869 individuals representing 3.3% of the adult population (mean [SD] age, 70.7 [14.5] years; 52.4% women; 39% non-White race/ethnicity). Latent class analysis resulted in a 7-class solution. Stakeholders defined the following complex patient profiles (prevalence): high acuity (9.4%), older patients with cardiovascular complications (15.9%), frail elderly (12.5%), pain management (12.3%), psychiatric illness (12.0%), cancer treatment (7.6%), and less engaged (27%). Patients in these groups had significantly different 1-year mortality rates (ranging from 3.0% for psychiatric illness profile to 23.4% for frail elderly profile; risk ratio, 7.9 [95% CI, 7.1-8.8], P < .001). Repeating the analysis using k-means clustering resulted in qualitatively similar groupings. Each clinical profile suggested a distinct collaborative care strategy to optimize management. The findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions.

Estimated life expectancy for older patients with diabetes informs decisions about treatment goals, cancer screening, long-term and advanced care, and inclusion in clinical trials. Easily implementable, evidence-based, diabetes-specific approaches for identifying patients with limited life expectancy are needed. Develop and validate an electronic health record (EHR)-based tool to identify older adults with diabetes who have limited life expectancy. Predictive modeling based on survival analysis using Cox-Gompertz models in a retrospective cohort. Adults with diabetes aged ≥ 65 years from Kaiser Permanente Northern California: a 2015 cohort (N = 121,396) with follow-up through 12/31/2019, randomly split into training (N = 97,085) and test (N = 24,311) sets. Validation was conducted in the test set and two temporally distinct cohorts: a 2010 cohort (n = 89,563; 10-year follow-up through 2019) and a 2019 cohort (n = 152,357; 2-year follow-up through 2020). Demographics, diagnoses, utilization and procedures, medications, behaviors and vital signs; mortality. In the training set (mean age 75 years; 49% women; 48% racial and ethnic minorities), 23% died during 5 years follow-up. A mortality prediction model was developed using 94 candidate variables, distilled into a life expectancy model with 11 input variables, and transformed into a risk-scoring tool, the Life Expectancy Estimator for Older Adults with Diabetes (LEAD). LEAD discriminated well in the test set (C-statistic = 0.78), 2010 cohort (C-statistic = 0.74), and 2019 cohort (C-statistic = 0.81); comparisons of observed and predicted survival curves indicated good calibration. LEAD estimates life expectancy in older adults with diabetes based on only 11 patient characteristics widely available in most EHRs and claims data. LEAD is simple and has potential application for shared decision-making, clinical trial inclusion, and resource allocation.

Background Minority racial/ethnic groups have low colorectal cancer (CRC) screening rates. Objective To evaluate a culturally tailored intervention to increase CRC screening, primarily using colonoscopy, among low income and non-English speaking patients. Design Randomized controlled trial conducted from January to October of 2007. Setting Single, urban community health center serving a low-income, ethnically diverse population. Patients A total of 1,223 patients 52-79 years of age overdue for CRC screening, randomized to intervention (n = 409) vs. usual care control (n = 814) groups. Intervention Intervention patients received an introductory letter with educational material followed by phone or in-person contact by a language-concordant “navigator.” Navigators (n = 5) were community health workers trained to identify and address patient-reported barriers to CRC screening. Individually tailored interventions included patient education, procedure scheduling, translation and explanation of bowel preparation, and help with transportation and insurance coverage. Rates of colorectal cancer screening were assessed for intervention and usual care control patients. Results Over a 9-month period, intervention patients were more likely to undergo CRC screening than control patients (27% vs. 12% for any CRC screening, p < 0.001; 21% vs. 10% for colonoscopy completion, p < 0.001). The higher screening rate resulted in the identification of 10.5 polyps per 100 patients in the intervention group vs. 6.8 in the control group (p = 0.04). Limitations Patients were from one health center. Some patients may have obtained CRC screening outside our system. Conclusions A culturally tailored, language-concordant navigator program designed to identify and overcome barriers to colorectal cancer screening can significantly improve colonoscopy rates for low income, ethnically and linguistically diverse patients. ClinicalTrials.gov registration number: NCT00476970

OBJECTIVE: To test if knowledge of type 2 diabetes genetic variants improves disease prediction. RESEARCH DESIGN AND METHODS: We tested 40 single nucleotide polymorphisms (SNPs) associated with diabetes in 3,471 Framingham Offspring Study subjects followed over 34 years using pooled logistic regression models stratified by age (<50 years, diabetes cases = 144; or ≥50 years, diabetes cases = 302). Models included clinical risk factors and a 40-SNP weighted genetic risk score. RESULTS: In people <50 years of age, the clinical risk factors model C-statistic was 0.908; the 40-SNP score increased it to 0.911 (P = 0.3; net reclassification improvement (NRI): 10.2%, P = 0.001). In people ≥50 years of age, the C-statistics without and with the score were 0.883 and 0.884 (P = 0.2; NRI: 0.4%). The risk per risk allele was higher in people <50 than ≥50 years of age (24 vs. 11%; P value for age interaction = 0.02). CONCLUSIONS: Knowledge of common genetic variation appropriately reclassifies younger people for type 2 diabetes risk beyond clinical risk factors but not older people.

Objectives: Financial strain has important consequences for patients, providers, and health care systems. However, there is currently no gold standard measure to screen for financial strain. This study compared the performance of 3 single-item screeners using a composite measure of financial strain as a “gold standard.” Methods: We conducted a secondary analysis of unweighted data from a 2021 survey of Kaiser Permanente Northern California health plan members comparing the percentages of adults who experienced financial strain based on 3 general single-item screeners, a screener specific to medical and dental health care use, and a composite financial strain measure. The study sample was comprised of 2734 non-Medicaid insured adults who answered all financial strain questions. Kappa statistics evaluating agreement of the 3 general screeners with the composite measure were calculated for the sample overall, by age group, and within age group, by 4 levels of income and 4 racial/ethnic subgroups. Results: Among 947 adults aged 35 to 65, 30.7% had just enough money or not enough money to make ends meet, 23.3% had a somewhat hard or hard time paying for basics, 18.8% had trouble paying for ≥1 type of expense, 20.5% had delayed/used less medical/dental care, and 41.5% had experienced financial strain based on the composite measure. Among 1787 adults aged 66 to 85, the percentages who screened positive on these measures were 22.7%, 19.4%, 12.9%, 19.8%, and 34.4%, respectively. Across the sample, by income categories and racial/ethnic groups, the making ends meet screener identified higher percentages of adults experiencing financial strain and performed better when compared with the composite measure than the hard to pay for the very basics and trouble paying for expenses screeners. Overall, substantial decreases in the percentages of adults who screened positive on the financial strain measures were seen as level of income increased. Within income categories, middle-aged adults were more likely than older adults to have experienced financial strain based on the composite and general single-item screeners. Conclusions: As social risk screening becomes part of the standard of care, it will be important to assess how well different brief screeners for financial strain perform with diverse patient populations.

ObjectivesTo evaluate associations between alcohol brief intervention (BI) in primary care and 12-month drinking outcomes and 18-month health outcomes among adults with hypertension and type 2 diabetes (T2D).DesignA population-based observational study using electronic health records data.SettingAn integrated healthcare system that implemented system-wide alcohol screening, BI and referral to treatment in adult primary care.ParticipantsAdult primary care patients with hypertension (N=72 979) or T2D (N=19 642) who screened positive for unhealthy alcohol use between 2014 and 2017.Main outcome measuresWe examined four drinking outcomes: changes in heavy drinking days/past 3 months, drinking days/week, drinks/drinking day and drinks/week from baseline to 12-month follow-up, based on results of alcohol screens conducted in routine care. Health outcome measures were changes in measured systolic and diastolic blood pressure (BP) and BP reduction ≥3 mm Hg at 18-month follow-up. For patients with T2D, we also examined change in glycohaemoglobin (HbA1c) level and ‘controlled HbA1c’ (HbA1c<8%) at 18-month follow-up.ResultsFor patients with hypertension, those who received BI had a modest but significant additional −0.06 reduction in drinks/drinking day (95% CI −0.11 to −0.01) and additional −0.30 reduction in drinks/week (95% CI −0.59 to −0.01) at 12 months, compared with those who did not. Patients with hypertension who received BI also had higher odds for having clinically meaningful reduction of diastolic BP at 18 months (OR 1.05, 95% CI 1.00 to 1.09). Among patients with T2D, no significant associations were found between BI and drinking or health outcomes examined.ConclusionsAlcohol BI holds promise for reducing drinking and helping to improve health outcomes among patients with hypertension who screened positive for unhealthy drinking. However, similar associations were not observed among patients with T2D. More research is needed to understand the heterogeneity across diverse subpopulations and to study BI’s long-term public health impact.

OBJECTIVE:--Diabetes eliminates the protective effect of female sex on the risk of coronary heart disease (CHD). We assessed sex differences in the treatment of CHD risk factors among patients with diabetes. RESEARCH DESIGN AND METHODS--A cross-sectional analysis included 3,849 patients with diabetes treated in five academic internal medicine practices from 2000 to 2003. Outcomes were stratified by the presence of CHD and included adjusted odds ratios (AORs) that women (relative to men) were treated with hypoglycemic, antihypertensive, lipid-lowering medications or aspirin (if indicated) and AORs of reaching target HbA[subscript 1c], blood pressure, or lipid levels. RESULTS:--Women were less likely than men to have HbA[subscript 1c] <7% (without CHD: AOR 0.84 [95% CI 0.75-0.95], P = 0.005; with CHD: 0.63 [0.53-0.75], P < 0.0001). Women without CHD were less likely than men to be treated with lipid-lowering medication (0.82 [0.71-0.96], P = 0.01) or, when treated, to have LDL cholesterol levels <100 mg/dl (0.75 [0.62-0.93], P = 0.004) and were less likely than men to be prescribed aspirin (0.63 [0.55-0.72], P < 0.0001). Women with diabetes and CHD were less likely than men to be prescribed aspirin (0.70 [0.60-0.83], P < 0.0001) or, when treated for hypertension or hyperlipidemia, were less likely to have blood pressure levels <130/80 mmHg (0.75 [0.69-0.82], P < 0.0001) or LDL cholesterol levels <100 mg/dl (0.80 [0.68-0.94], P = 0.006). CONCLUSIONS:--Women with diabetes received less treatment for many modifiable CHD risk factors than diabetic men. More aggressive treatment of CHD risk factors in this population offers a specific target for improvement in diabetes care.

To compare electronically monitored (MEMS) with self-reported adherence in drug users, including the impact of adherence on HIV load, we conducted a 6-month observational study of 67 antiretroviral-experienced current and former drug users. Adherence (percentage of doses taken as prescribed) was calculated for both the day and the week preceding each of 6 research visits. Mean self-reported 1-day adherence was 79% (median, 86%), and mean self-reported 1-week adherence was 78% (median, 85%). Mean MEMS 1-day adherence was 57% (median, 52%), and mean MEMS 1-week adherence was 53% (median, 49%). One-day and 1-week estimates were highly correlated (r > .8 for both measures). Both self-reported and MEMS adherence were correlated with concurrent HIV load (r = .43–.60), but the likelihood of achieving virologic suppression was greater if MEMS adherence was high than if self-reported adherence was high. We conclude that self-reported adherence is higher than MEMS adherence, but a strong relationship exists between both measures and virus load. However, electronic monitoring is more sensitive than self-report for the detection of nonadherence and should be used in adherence intervention studies.

OBJECTIVE:--The purpose of this study was to examine medication adherence and other self-management practices as potential determinants of higher glycemic risk among black relative to white patients. RESEARCH DESIGN AND METHODS--We used a retrospective, longitudinal repeated-measures design to model the contribution of medication adherence to black-white differences in A1C among type 2 diabetic patients at a large multispecialty group practice. We identified 1,806 adult (aged >=18 at diagnosis) patients (467 black and 1,339 white) with newly initiated oral hypoglycemic therapy between 1 December 1994 and 31 December 2000. Race was identified using an electronic medical record and patient self-report. Baseline was defined as the 13 months preceding and included the month of therapy initiation. All patients were required to have at least 12 months of follow-up. RESULTS:--At initiation of therapy, black patients had higher average A1C values compared with whites (9.8 vs. 8.9, a difference of 0.88; P < 0.0001). Blacks had lower average medication adherence during the first year of therapy (72 vs. 78%; P < 0.0001). Although more frequent medication refills were associated with lower average A1C values, adjustment for adherence did not eliminate the black-white gap. CONCLUSIONS:--We found persistent racial differences in A1C that were not explained by differences in medication adherence. Our findings suggest that targeting medication adherence alone is unlikely to reduce disparities in glycemic control in this setting. Further research is needed to explore possible genetic and environmental determinants of higher A1C among blacks at diagnosis, which may represent a critical period for more intensive intervention.